Data Standards and Research Reproducibility
The LSP is strongly committed to FAIR (Findable, Accessible, Interoperable and Reusable) research.
We thoughtfully consider the factors that influence the reproducibility of laboratory-based research findings and advocate for solutions. Whenever feasible, LSP results and software are open-source, and data is available under public domain (i.e., Creative Commons) licenses. We also run a seminar series that features speakers at the cutting edge of data and knowledge management.
LSP investigators have studied the irreproducibility of preclinical drug response and pharmacodynamic data in detail (Niepel, 2019) and developed multiple methods to address the problem (Hafner, 2016; Mills, 2022), commented on the importance of public data release for reproducibility (AlQuraishi, 2016), and developed methods to liberate survival data about from clinical trials from pictorial representations (Plana, 2022).
In the emerging field of multiplexed imaging, we have created open-source data processing pipelines to increase the reliability of complex data analysis (Schapiro, 2022a), established a metadata scheme for standardizing the description of tissue images (Schapiro, 2022b), and developed the first quality control software for high-plex imaging data (Baker, 2024).
We also developed Minerva, a lightweight software that makes it possible to share whole slide multiplex images online without download (Hoffer, 2020; Rashid, 2022). We've partnered with external organizations like cBioPortal (Wala, 2024) and the Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) (De Bruijn, 2024) to enable large scale tissue atlases by incorporating the Minerva image viewer into existing data repositories.
